Recently, machine vision is gaining attention in food science as well as in food industry concerning\nfood quality assessment and monitoring. Into the framework of implementation of\nProcess Analytical Technology (PAT) in the food industry, image processing can be used\nnot only in estimation and even prediction of food quality but also in detection of adulteration.\nTowards these applications on food science, we present here a novel methodology for\nautomated image analysis of several kinds of food products e.g. meat, vanilla cr�¨me and\ntable olives, so as to increase objectivity, data reproducibility, low cost information extraction\nand faster quality assessment, without human intervention. Image processingâ��s outcome\nwill be propagated to the downstream analysis. The developed multispectral image\nprocessing method is based on unsupervised machine learning approach (Gaussian Mixture\nModels) and a novel unsupervised scheme of spectral band selection for segmentation\nprocess optimization. Through the evaluation we prove its efficiency and robustness against\nthe currently available semi-manual software, showing that the developed method is a high\nthroughput approach appropriate for massive data extraction from food samples.
Loading....